



'''simple docstring'''

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						Dict	=  {
    '''microsoft/unispeech-sat-base-100h-libri-ft''': (
        '''https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'''
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}

class 							a__( snake_case__     ):
			a_	: Optional[Any]									=      '''unispeech-sat'''






			def __init__(     self					,   _UpperCAmelCase=32					,   _UpperCAmelCase=768					,   _UpperCAmelCase=12					,   _UpperCAmelCase=12					,   _UpperCAmelCase=3072					,   _UpperCAmelCase="gelu"					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=0.0					,   _UpperCAmelCase=0.0					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=0.02					,   _UpperCAmelCase=1E-5					,   _UpperCAmelCase="group"					,   _UpperCAmelCase="gelu"					,   _UpperCAmelCase=(512, 512, 512, 512, 512, 512, 512)					,   _UpperCAmelCase=(5, 2, 2, 2, 2, 2, 2)					,   _UpperCAmelCase=(10, 3, 3, 3, 3, 2, 2)					,   _UpperCAmelCase=False					,   _UpperCAmelCase=128					,   _UpperCAmelCase=16					,   _UpperCAmelCase=False					,   _UpperCAmelCase=True					,   _UpperCAmelCase=0.05					,   _UpperCAmelCase=10					,   _UpperCAmelCase=2					,   _UpperCAmelCase=0.0					,   _UpperCAmelCase=10					,   _UpperCAmelCase=0					,   _UpperCAmelCase=320					,   _UpperCAmelCase=2					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase=100					,   _UpperCAmelCase=256					,   _UpperCAmelCase=256					,   _UpperCAmelCase=0.1					,   _UpperCAmelCase="mean"					,   _UpperCAmelCase=False					,   _UpperCAmelCase=False					,   _UpperCAmelCase=256					,   _UpperCAmelCase=(512, 512, 512, 512, 1500)					,   _UpperCAmelCase=(5, 3, 3, 1, 1)					,   _UpperCAmelCase=(1, 2, 3, 1, 1)					,   _UpperCAmelCase=512					,   _UpperCAmelCase=0					,   _UpperCAmelCase=1					,   _UpperCAmelCase=2					,   _UpperCAmelCase=504					,   **_UpperCAmelCase					,   )							->							List[str]:
					super().__init__(**_UpperCAmelCase					,   pad_token_id=_UpperCAmelCase					,   bos_token_id=_UpperCAmelCase					,   eos_token_id=_UpperCAmelCase						)
					snake_case__	     =hidden_size
					snake_case__	     =feat_extract_norm
					snake_case__	     =feat_extract_activation
					snake_case__	     =list(_UpperCAmelCase						)
					snake_case__	     =list(_UpperCAmelCase						)
					snake_case__	     =list(_UpperCAmelCase						)
					snake_case__	     =conv_bias
					snake_case__	     =num_conv_pos_embeddings
					snake_case__	     =num_conv_pos_embedding_groups
					snake_case__	     =len(self.conv_dim						)
					snake_case__	     =num_hidden_layers
					snake_case__	     =intermediate_size
					snake_case__	     =hidden_act
					snake_case__	     =num_attention_heads
					snake_case__	     =hidden_dropout
					snake_case__	     =attention_dropout
					snake_case__	     =activation_dropout
					snake_case__	     =feat_proj_dropout
					snake_case__	     =final_dropout
					snake_case__	     =layerdrop
					snake_case__	     =layer_norm_eps
					snake_case__	     =initializer_range
					snake_case__	     =vocab_size
					snake_case__	     =num_clusters
					snake_case__	     =do_stable_layer_norm
					snake_case__	     =use_weighted_layer_sum

					if (
					    (len(self.conv_stride						) != self.num_feat_extract_layers)
					    or (len(self.conv_kernel						) != self.num_feat_extract_layers)
					    or (len(self.conv_dim						) != self.num_feat_extract_layers)
					):
							raise ValueError(
							    'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
							    ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
							    f""" {len(self.conv_dim						)}`, `len(config.conv_stride) = {len(self.conv_stride						)}`,"""
							    f""" `len(config.conv_kernel) = {len(self.conv_kernel						)}`."""						)

					# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
					snake_case__	     =apply_spec_augment
					snake_case__	     =mask_time_prob
					snake_case__	     =mask_time_length
					snake_case__	     =mask_time_min_masks
					snake_case__	     =mask_feature_prob
					snake_case__	     =mask_feature_length
					snake_case__	     =mask_feature_min_masks

					# parameters for pretraining with codevector quantized representations
					snake_case__	     =num_codevectors_per_group
					snake_case__	     =num_codevector_groups
					snake_case__	     =contrastive_logits_temperature
					snake_case__	     =feat_quantizer_dropout
					snake_case__	     =num_negatives
					snake_case__	     =codevector_dim
					snake_case__	     =proj_codevector_dim
					snake_case__	     =diversity_loss_weight

					# ctc loss
					snake_case__	     =ctc_loss_reduction
					snake_case__	     =ctc_zero_infinity

					# SequenceClassification-specific parameter. Feel free to ignore for other classes.
					snake_case__	     =classifier_proj_size

					# XVector-specific parameters. Feel free to ignore for other classes.
					snake_case__	     =list(_UpperCAmelCase						)
					snake_case__	     =list(_UpperCAmelCase						)
					snake_case__	     =list(_UpperCAmelCase						)
					snake_case__	     =xvector_output_dim






			@property
			def 						_lowercase  (     self						)							->							int:
					return functools.reduce(operator.mul					,   self.conv_stride					,   1						)









'''simple docstring'''

from __future__ import annotations






def 				a  (     UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    list[int]      ,					UpperCamelCase_    :    int			)       ->	tuple[float, list[float]]:
		snake_case__	     =list(range(len(UpperCamelCase_			)			)			)
		snake_case__	     =[v / w for v, w in zip(UpperCamelCase_      ,					UpperCamelCase_			)]
		index.sort(key=lambda UpperCamelCase_			: ratio[i]      ,					reverse=UpperCamelCase_			)

		snake_case__	     =0
		snake_case__	     =[0] * len(UpperCamelCase_			)
		for i in index:
				if weight[i] <= capacity:
						snake_case__	     =1
						max_value += value[i]
						capacity -= weight[i]
				else:
						snake_case__	     =capacity / weight[i]
						max_value += value[i] * capacity / weight[i]
						break

		return max_value, fractions


if __name__ == "__main__":
			import doctest

			doctest.testmod()




